docstr: convert the sentences into lists of tokens, pad them to the same length, add bos tokens if it is needed
  Args:
      sentences (List[str]): list of input sentences in str format.
      max_len (int): max number of tokens to generate.
      add_BOS (bool): whether to add the BOS token at the beginning
  Returns:
      Tuple[torch.Tensor], the tokenized and padded torch tensor and the token context length tensor.
docstr_tok: ["convert","the","sentences","into","lists","of","tokens",",","pad","them","to","the","same","length",",","add","bos","tokens","if","it","is","needed","Args",":","sentences","(","List","[","str","]",")",":","list","of","input","sentences","in","str","format",".","max_len","(","int",")",":","max","number","of","tokens","to","generate",".","add_BOS","(","bool",")",":","whether","to","add","the","BOS","token","at","the","beginning","Returns",":","Tuple","[","torch.Tensor","]",",","the","tokenized","and","padded","torch","tensor","and","the","token","context","length","tensor","."]
code: def tokenize_batch_with_context_and_completion(
      self, sentences, max_len, add_BOS
  ):
      """
      convert the sentences into lists of tokens, pad them to the same length, add bos tokens if it is needed
      Args:
          sentences (List[str]): list of input sentences in str format.
          max_len (int): max number of tokens to generate.
          add_BOS (bool): whether to add the BOS token at the beginning
      Returns:
          Tuple[torch.Tensor], the tokenized and padded torch tensor and the token context length tensor.
      """
      tokenizer = self.model.tokenizer
      if add_BOS:
          context_tokens = [
              [
                  [tokenizer.bos_id] + tokenizer.text_to_ids(s[0]),
                  tokenizer.text_to_ids(s[1]),
              ]
              for s in sentences
          ]
      else:
          context_tokens = [
              [tokenizer.text_to_ids(s[0]), tokenizer.text_to_ids(s[1])]
              for s in sentences
          ]
      if self.pad_token_for_retrieval:
          padded = []
          for line in context_tokens:
              if len(line[0]) < self.chunk_size:
                  pad_len = self.chunk_size - len(line[0])
                  if self.megatron_lm_compatible:
                      # megatron lm use eos to pad
                      padded.append(
                          [tokenizer.eos_id] * pad_len
                          + line[0]
                          + line[1]
                      )
                  else:
                      padded.append(
                          [tokenizer.pad_id] * pad_len
                          + line[0]
                          + line[1]
                      )
              else:
                  padded.append(line[0] + line[1])
          context_tokens = padded
      context_tokens, context_lengths = pad_batch(
          context_tokens, tokenizer.eos_id, max_len
      )
      context_tokens_tensor = torch.cuda.LongTensor(context_tokens)
      context_length_tensor = torch.cuda.LongTensor(context_lengths)
      return context_tokens_tensor, context_length_tensor
  
imports: import json
  import pickle
  from typing import List, Tuple
  import numpy
  import torch
  import torch.distributed
  from nemo.collections.nlp.modules.common.lm_utils import pad_batch
  from nemo.collections.nlp.modules.common.megatron.retrieval_services.retrieval_service import ComboRetrievalService
  from nemo.collections.nlp.modules.common.text_generation_strategy import TextGenerationStrategy
label_desc: Use image node_id 5 for calling the RetroModelTextGenerationStrategy obj's underlying member method code with example usage: obj.tokenize_batch_with_context_and_completion(sentences, max_len, add_BOS) and returns: context_tokens_tensor,  context_length_tensor